Discrimination of Pb-Zn deposit types using sphalerite geochemistry: New insights from machine learning algorithm  

作　　者：Xiao-Ming Li Yi-Xin Zhang Zhan-Ke Li Xin-Fu Zhao Ren-Guang Zuo Fan Xiao Yi Zheng 

机构地区：[1]School of Earth Resources,China University of Geosciences,Wuhan,Hubei Province 430074,China [2]School of Computer Science,China University of Geosciences,Wuhan,Hubei Province 430078,China [3]State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan 430074,China [4]School of Earth Sciences and Engineering,Sun Yat-Sen University,Zhuhai,Guangdong Province 519000,China

出　　处：《Geoscience Frontiers》2023年第4期200-219,共20页地学前缘（英文版）

基　　金：We would like to acknowledge the financial support of the Ministry of Science and Technology of China(Grant No.2021YFC2900300);the National Natural Science Foundation of China(Grant Nos.41772074 and 42172103).

摘　　要：Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe

关 键 词：DISCRIMINATION Pb-Zn deposit Sphalerite trace elements Machine learning algorithms Feature analysis 

分 类 号：U455.43[建筑科学—桥梁与隧道工程]